Integrated Proteomic and Metabolomic Analyses Provide Insights Into Acquisition of Embryogenic Ability in Agapanthus praecox

Somatic embryogenesis (SE) is an ideal model for plant cell totipotency. Transition from somatic cells to embryogenic cells is the key to SE. The poor frequency of embryogenic callus (EC) induction has limited the application of SE in many plants, such as Agapanthus praecox. We performed large-scale, quantitative proteomic and metabolomic analyses with different callus differentiation directions (SE and organogenesis) and stages (initial SE and repetitive SE) to better understand the morphological, physiological, and molecular characteristics of the acquisition of embryogenic ability in A. praecox. Integrated proteomic and metabolomic analyses suggested that callus differentiation direction was potentially regulated by pathways related to carbohydrate and energy metabolism (fatty acid metabolism, pyruvate metabolism, glycolysis/gluconeogenesis, pentose and glucuronate interconversions, starch and sucrose metabolism, galactose metabolism, carbon fixation pathways in prokaryotes, carbohydrate digestion and absorption, and fructose and mannose metabolism), chromatin accessibility and DNA methylation, reactive oxygen species responses and resistance (ascorbate and aldarate metabolism), and plant hormonal signaling. As a validation, we found that carbon source combination and plant hormone regulation in the culture medium significantly affected the acquisition of embryogenic ability, thereby inducing EC. Interestingly, plant hormonal signaling-related genes showed different expression patterns significantly when callus cultured with different carbon sources. Thus, our results suggested that energy supply and hormone signal transduction seemed to cooperatively contribute to the activation of embryogenic ability. Altogether, this study revealed valuable information regarding the molecular and biochemical changes that occurred during EC induction and provided valuable foundation for comprehensive understanding of the mechanisms associated with SE and organogenesis in A. praecox.

INTRODUCTION
Plants display a remarkable capacity for somatic cell totipotency, as demonstrated by a single somatic cell prerequisite for reprogramming, and then develop into complete plants (Guo et al., 2019;Wang et al., 2020). Somatic embryogenesis (SE) is a cell differentiation process involved in dedifferentiation and redifferentiation through the reconstruction of somatic cells to generate somatic embryos (SEs; Yang and Zhang, 2010). SE is an important model for studying the totipotency of somatic cells in plants (Fehér, 2015;Su et al., 2021). Based on cellular totipotency, the embryogenic callus (EC) showed great superiority for micropropagation, cryopreservation, genetic transformation, embryological research in plants (Zhou et al., 2016;Wang et al., 2020). Much progress has been made in the elucidation of the underlying mechanisms of SE (Fehér, 2015;Horstman et al., 2017;Su et al., 2021). However, EC induction and plant regeneration are affected by many factors, including genotypes, explants, hormones, and concentrations of various substances in the induction medium (Guo et al., 2020;Wang et al., 2020Wang et al., , 2022. So far, the acquisition of EC has been shown as the critical process in plant SE (Wang et al., 2022). However, it is difficult to identify the cells capable of embryogenesis with morphological data. During the somatic to embryogenic transition, the cells must dedifferentiate, activate their cell division cycle, and reorganize their metabolic and physiological states (Kumaravel et al., 2017;Wang et al., 2020). Cell dedifferentiation results in the totipotency fate determination of somatic plant cells and embryogenesis initiation (Guo et al., 2020). The transition phase toward competent and embryogenic cell types is much less defined so far. Therefore, it is critical to track and dissect the specific cellular events associated with the acquisition of embryogenic competence in such highly refined systems (Guo et al., 2019).
The development and application of omics technology, including transcriptomics, proteomics, and metabolomics, have significantly promoted the elucidation of mechanisms concerning plant SE in recent years (Marsoni et al., 2008;Yin et al., 2008;Kumaravel et al., 2017;Sun et al., 2018;Liu et al., 2019). However, both commonalities and personalities were identified within plants (Aguilar-Hernández and Loyola-Vargas, 2018;Tang et al., 2020;Wójcikowska et al., 2020). It was found that SE relative differentially expressed genes (DEGs) were mainly enriched in hormone synthesis and signal-related pathways in Catalpa bungei and Gossypium hirsutum (Sun et al., 2018;Liu et al., 2019). The interaction between hormones and stress signal-related differentially accumulated proteins (DAPs) was indispensable in the embryogenic fate determination in Musa nana (Kumaravel et al., 2017), while DAPs related to SE were mainly enriched in cell differentiation, stress, and glucose metabolism in Zea mays (Sun et al., 2013). Besides, proteins related to cellular metabolism, carbohydrate metabolism, oxidative stress response, glycolysis, and hormones were differentially accumulated during callus (CA) differentiation in Oryza sativa and Vitis vinifera (Marsoni et al., 2008;Yin et al., 2008). Moreover, a study combining high-throughput proteomics and metabolomics revealed the dynamic and complex network of EC formation in plants, which showed strong evidence of the capacity for embryogenic ability acquisition (Ge et al., 2017;Su et al., 2021).
Agapanthus praecox is a monocotyledonous, herbaceous, and perennial plant. This species is well known for its ornamental qualities, which include cut flowers, potted plants, and floral border plants in the landscape. In A. praecox, the initial flowering takes 3-4 years, and then, flowering occurs annually thereafter. Seed propagation is time-consuming and limited by character segregation in this species. Therefore, it is necessary to establish an efficient regeneration system because of the longer reproductive cycle. To date, the organogenesis and SE pathway were established in A. praecox preliminarily. CA was always induced by picloram (PIC), and then the organs (usually buds and roots) and EC could be induced. However, CA cells acquired a specific competence state to achieve regeneration ability (totipotency and pluripotency; Fehér, 2015;Wójcikowska et al., 2020). The organs always regenerated from the interior or surface of the initial organogenesis-based callus (IOC), while initial EC emerged from the surface of the initial embryogenic cell-originated callus (IEC). Besides, repetitive EC was gained from the repetitive embryogenic cell-originated callus (REC). Interestingly, the CA turned into a gradual embryogenic cell-originated callus (GEC), which showed relative opacity and slightly yellowish color, and then induced a few numbers of SEs (Figure 1). However, despite several published studies on SE and organogenesis in A. praecox, little information was gained concerning the mechanisms of CA differentiation direction and stage in A. praecox. The co-existence of multiple regeneration modes indicated that A. praecox was an unusual model to study the mechanism of the acquisition of embryogenic ability in plants.
SEs developed when EC was exposed to exogenous auxinfree conditions (Su et al., 2021), with few barriers to plant regeneration in A. praecox. However, the acquisition of EC became a huge difficulty, which showed randomness and inefficiency (Aguilar-Hernández and Loyola-Vargas, 2018). Many plants face a similar challenge, including even some important crops, such as O. sativa, Z. mays, G. hirsutum, and Triticum aestivum (Yin et al., 2008;Yang et al., 2014;Guo et al., 2019Guo et al., , 2020Wang et al., 2022). Therefore, the transition from CA to EC is the key to SE. Also, the lacking of a stable and accurate EC induction method is the bottleneck that restricts the theoretical research and application of SE. Further, the mechanism of embryogenic fate determination during the EC induction process is a key scientific question that has not been addressed yet in many plants (Aguilar-Hernández and Loyola-Vargas, 2018;Wang et al., 2020). The regulatory roles of chromatin accessibility, phytohormones, and stress-related events during SE in Arabidopsis thaliana have been well studied (Su et al., 2009;Horstman et al., 2017;Wang et al., 2020). So far, few studies have revealed the mechanisms comprehensively, especially the induction of EC, due to the complexity and individuation of SE (Wang et al., 2022). Proteins and metabolites are known as the active controllers of cellular physiology (Wilinski et al., 2019;Castander-Olarieta et al., 2021). The combination of proteomics and metabolomics can reveal the interactions that initiate the transitions between physiological states (Ribeiro et al., 2019), but the influence of proteins and metabolites related to EC formation in A. praecox remains unclear.
The objective of this study was to gain insights into the molecular changes leading to the cell differentiation directions (IEC, IOC, and GEC) and stages (IEC and REC) of A. praecox, especially the acquisition of embryogenic ability in A. praecox. Detailed knowledge of protein accumulation and metabolite accumulation profiles associated with the embryogenic state Frontiers in Plant Science | www.frontiersin.org may help in improving EC initiation in A. praecox. For this, we compared samples including CA and EC originating from a single-cell line and strictly initiated and then proliferated them in the same media and under the same environmental conditions. We applied a novel approach involving integrated multi-scale analyses, combining proteomic and metabolomic profiling, followed by KEGG pathway XML (KGML) analyses to elucidate the differences in functions and interactions of significant factors (proteins and metabolites) related to embryogenic ability determination. We also verified the effects of important pathways on cell differentiation direction, which could improve EC induction specifically in A. praecox.

Plant Material and Culture Conditions
The CA of A. praecox was induced using pedicel as explants. CA was induced and transferred monthly on MS medium containing 1.5 mg L −1 PIC and 30 g L −1 sucrose. CA and EC were cultured in the phytotron in the dark at 25 ± 1°C. Single kind (sucrose, glucose, and maltose) or combination (sucrose and maltose) of carbon sources were employed in the confirmatory experiment. CA was subcultured on MS medium contained 1.5 mg L −1 PIC and 30 g L −1 carbon source to investigate the effects of sugars on cell differentiation direction. Nine cell masses (approximately 1 g) were planted per Petri dish, and each replicate contained ten Petri dishes, and three biological replicates were conducted. The data was collected after 30 days.

Morphological Microscopy Observation
For cell morphological observation, CA, EC, and organ morphological image was captured using Stereo Discovery V20 Macro Stereo (Carl Zeiss, Germany). For cell microscopy observation, fresh CA and EC blocks (<5 mm 3 ) were transferred to a 1.5-ml Eppendorf tube immediately, and the samples were suspended in 0.5 ml of 1% (w/v) acetocarmine for 30 min. Then the CA and EC suspension was diluted with ddH 2 O, the suspension including CA and EC cells (<0.5 mm 3 ) was transferred to a slide, and a cover glass was placed on the slide slightly. Microscopic observations were performed using an Axio Scope A1 microscope (Carl Zeiss, Germany). The cell diameter was calculated using 10 individual cells.

Proteome Profiling
Proteins were extracted from three biological replicates per sample, and 0.1 g of the frozen samples were placed in a cold mortar. A mortar and a pestle were used to grind the tissue into a fine powder, and 1 ml of Tris-phenol buffer was added to incubate at room temperature for 10 min. Then, 1 ml of saturated phenol was added, and the mixture was shaken for 40 min at 4°C. The tubes were centrifuged at 5,000 g for 15 min at 4°C, and the upper phenolic phase was collected. Subsequently, cold 0.1 M ammonium acetate-methanol solution was added at −20°C using five volumes of the collected phenolic phase.
The sediment was collected after centrifugation at 12,000 g for 10 min at 4°C. Then, the sediment was collected, repeating this step one more time, and dried at room temperature for 2 min. Subsequently, the sediment was resuspended in 300 μl of lysate solution [8 M urea and 40 mm Tris-HCl containing 1 mM phenylmethylsulfonyl fluoride (PMSF), 5 mm dithiothreitol (DTT), and 2 mm ethylenediaminetetraacetic acid (EDTA)] for 3 h. Finally, the supernatant was the extracted protein solution after centrifugation of mixtures at 12,000 g for 10 min at room temperature. The measurements of protein were performed by the Bradford method. TMT labeling was performed according to the FASP method (Jiang et al., 2021). For TMTpro 16 labeling, the lyophilized samples were resuspended in 100 μl of 100 mm TEAB, pH 8.5, and 40 μl of each sample were transferred to new tubes for labeling. Anhydrous acetonitrile was added to the TMT reagent vial at room temperature. The centrifuged reagents were dissolved for 5 min and mixed for centrifugation; this step was repeated once. Then, 10 μl of the TMTpro label reagent was added to each sample for mixing. The tubes were incubated at room temperature for 1 h. Finally, 5 μl of 5% hydroxylamine was added to each sample and incubated for 15 min to quench the reaction. The labeling peptides solutions were lyophilized and stored at −80°C. All samples were analyzed using a Triple TOF 5600 mass spectrometer (SCIEX, United States). The flow rate of the Eksigent nanoLC-1D plus system (SCIEX) was 300 μl/min, and the linear gradient was 90 min (from 5 to 85% B over 67 min; mobile phase A = 2% ACN/0.1% FA and B = 95% ACN/0.1% FA). A rolling collision energy voltage was used for CID fragmentation for MS/MS spectra acquisitions. The mass was dynamically excluded for 22 s. Enrichment analysis of the proteins was performed using the cloud platform of OmicsBean to analyze the functional characteristics of the selected DAPs. The LC-MS/MS raw data were imported in MaxQuant for analysis. The database was offered by Proteome Discover 2.4 (Thermo Fisher). Proteins with |log 2 -fold change| > 1 and P adj < 0.05 were considered DAPs. For this analysis, protein IDs assigned was based on the homology of KO (KEGG Orthology), and the online Kyoto Encyclopedia of Genes and Genomes (KEGG) 1 was used to classify the identified proteins. In addition, non-commercial databases, including metabolite pathways, were searched on KEGG.

Metabolome Profiling
The fresh samples (80 mg) were transferred to a 1.5-ml Eppendorf tube. Two small steel balls were added to the tube. Further, 360 μl of cold methanol and 40 μl of 2-chloro-l-phenylalanine (0.3 mg/ml) dissolved in methanol as the internal standard were added to each sample. The samples were placed at −80°C for 2 min and ground at 60 Hz for 2 min. The mixtures were ultrasonicated at ambient temperature for 30 min. Then, 200 μl of chloroform was added to the samples, the mixtures were vortexed, and 400 μl of water was added. The samples were vortexed again and then ultrasonicated at ambient temperature Frontiers in Plant Science | www.frontiersin.org for 30 min. They were centrifuged at 13,000 rpm for 10 min at 4°C. The QC sample was prepared by mixing aliquots of all samples to be a pooled sample. An aliquot of the 200-μL supernatant was transferred to a glass sampling vial for vacuumdrying at room temperature. Then, 80 μl of 15 mg/ml methoxylamine hydrochloride in pyridine was subsequently added. The resultant mixture was vortexed for 2 min and incubated at 37°C for 90 min. Next, 80 μl of BSTFA (with 1% TMCS) and 20 μl of n-hexane were added to the mixture, vortexed for 2 min, and then derivatized at 70°C for 60 min. The samples were placed at ambient temperature for 30 min before GC-MS analysis. The derivatized samples were analyzed on an Agilent 7890B gas chromatography system coupled to an Agilent 5977A MSD system (Agilent Technologies Inc., CA, United States). A DB-5MS fused-silica capillary column (30 m × 0.25 mm × 0.25 μm, Agilent J & W Scientific, CA, United States) was used to separate the derivatives. Helium (>99.999%) was used as the carrier gas at a constant flow rate of 1 ml/min through the column. The injector temperature was maintained at 260°C. The injection volume was 1.0 μl in the split-less mode. The initial oven temperature was 60°C, ramped to 125°C at a rate of 8°C/min, to 210°C at a rate of 4°C/min, to 270°C at a rate of 5°C/min, to 305°C at a rate of 10°C/min, and finally held at 305°C for 3 min. The temperature of MS quadrupole and ion source (electron impact) was set to 150, and 230°C, respectively. The collision energy was 70 eV. The mass data were acquired in a full-scan mode (m/z 50-500). ChemStation software (version E.02.02.1431, Agilent, United States) was used to acquire and preprocess the data. Metabolites were annotated based on the Fiehn or NIST databases. After alignment with statistic compare component, the raw data array was obtained from raw data with three dimensions data sets including sample information, peak names, and peak intensities. In the data array, all internal standards and any known pseudo positive peaks were removed. After RSD of the interior label >0.3 deleted, all peak strength (peak area) was processed by normalization of multi-interior label according to retention time partition period. Data were transformed by log 10 , and the generated data matrix was imported into the SIMCA 14.0 software package (Umetrics, Umeå, Sweden). PCA and (orthogonal) partial least squares-discriminant analysis [(O) PLS-DA] were performed to visualize the metabolic difference among experimental groups after mean centering and unit variance scaling. Variable importance in the projection (VIP) ranked the overall contribution of each variable to the OPLS-DA model, and variables with VIP > 1 were considered relevant for group discrimination. Metabolites with p ≤ 0.05 and VIP ≥ 1.0 were considered significant differentially accumulated metabolites (DAMs). KEGG pathway analysis in metabolites was performed by OE Biotech. Co., Ltd. (Shanghai, China). 2

Integrated Proteome and Metabolome Analysis
The proteins and corresponding metabolites were considered to be correlated if they were both accumulated in the same 2 https://www.oebiotech.com/ sample. Based on the log 2 fold change of DAPs and DAMs, the Spearman correlation coefficients and associated p-values were calculated; the correlation plots of comparative analyses were also drawn. KEGG pathway enrichment analyses were then visualized. Moreover, the cognate DAPs and DAMs were mapped to the reference pathways in the KEGG database to better understand the regulatory status of DAPs and DAMs in pathways.

Transcriptome Sequencing
Total RNA was extracted using an RNAprep Pure Plant Kit (Tiangen, China). The integrity of the extracted RNA was verified on an Agilent 2,100 Bioanalyzer (Agilent Technologies, CA, United States). The cDNA library construction and transcriptome sequencing were performed by Origin-gene Biotech. Co., Ltd. (Shanghai, China) 3 on an Illumina HiSeq 2,500 platform, and paired-end reads were generated. The functions of unigenes were annotated based on public databases, including NCBI non-redundant (NR), SwissProt, KEGG pathway, Clusters of Orthologous Groups for eukaryotic complete genomes (KOG), and the GO database using an e-value of 10 −5 . The relationship between the SwissProt and GO terms was mapped for GO classification, based on the SwissProt annotation. The KEGG database was used to annotate potential unigene metabolic pathways. Genes with |log 2 fold change| > 1 and P adj < 0.05 were considered DEGs.

Statistical Analysis
The data including callus multiplication, cell mass, cell size was statistically tested using one-way analysis of variance with SPSS software (SPSS Inc., IL, United States; v20.0). The significance was shown with different lowercase letters at p < 0.05.

Overview of Morphological, Proteomic, and Metabolic Analyses for Cell Differentiation
Samples with different differentiation directions and stages are demonstrated in Figure 1A. The CA was usually whitish, semitransparent, surfacing, and watery. The IOC showed similar texture and color with the CA, but it could generate a bud primordium (BP), which was opaque and granular. The GEC was relatively opaque and slightly yellowish compared with the CA, and with a rough surface. The IEC generated small clumps and a compact, bright yellow cell mass, which showed a high proliferation rate and cell viability. Also, cell divisions were usually more frequent in embryogenic lines, which grew more rapidly than in non-EC. Accordingly, the microscopic comparison revealed large numbers of mitotic meristematic cells in IEC (Figures 1B,C). REC produced loose, yellow clumps of cells. SEC was friable in texture, rough in surface, and yellow in color. Non-EC and EC were demonstrated clearly by acetocarmine staining. The CA, IOC, and GEC had large cell diameters and thin cytoplasm, while the IEC, REC, and SEC had small cell diameters and dense cytoplasm. Interestingly, the cell size of EC generated from the IEC was significantly reduced compared with that of the CA (Figures 1B,C). Samples related to different differentiation directions and stages showed widely different regeneration efficiency when cultured on a PIC-free medium. The CA tended to be light brown because of the absence of exogenous auxin, and adventitious buds and SE were induced from the IOC and IEC, respectively. A few numbers of SEs were induced in the sample of the GEC. The REC regenerated SE spontaneously, but it was accompanied by lower SE efficiency. Most of the SEs showed an abnormal polarity structure in REC. The SEC induced SEs naturally, but the SE number decreased appreciably compared with those induced by the IEC (Figure 1D). The regeneration efficiency showed huge differences among the samples, which indicated that IEC is the ideal model for plant regeneration in intro in A. praecox ( Figure 1E).
The samples were divided into two groups by TMTpro 16 labeling quantitative proteomics. The quantitative analysis showed that 12,509 unique peptides corresponding to 4,221 proteins were identified in group 1, and 12,555 unique peptides corresponding to 4,203 proteins were authenticated in group 2. We identified 36-492 DAPs between different comparison groups. The comparison group of SEC/CA showed huge differences, while the comparison group of IEC/SEC showed little differences (Figure 2A). The statistical analysis of metabolites showed that the DAMs ranged from 93 to 127 between different comparison groups. The comparison group of SEC/CA showed huge differences, while the comparison group of IOC/IEC showed little differences ( Figure 2B).
The principal component analysis (PCA) model of proteomics on our paired datasets showed a high concordance between three replicates. Samples from different differentiation directions and stages were separated by PC1 (39.45%) and PC2 (23.11%). CA was more dispersive than the other samples, implying that protein accumulation patterns varied during CA differentiation. In addition, non-EC samples from different groups could be distinctly separated by PC1, indicating that EC and non-EC exhibited accumulation variation at the same stage ( Figure 2C). We mixed (in equal proportions) samples to create QC samples to test the stability of the mass spectrometry system. The PCA model diagram showed that the QC samples were closely clustered, indicating that the experiment was stable and repeatable ( Figure 2D). The PCA model for metabolomics on our paired datasets showed high concordance between six replicates. In addition, non-EC samples from different groups could be distinctly separated by PC1 (27.0%), while the initial differentiation samples (IOC, IEC, and GEC) were distinctly separated by PC2 (19.9%). The results indicated that the PCA analysis of metabolites was highly consistent with the PCA model in proteomic profiles.
The results indicated that CA samples underwent complex diversity concerning cell differentiation. Interestingly, morphological, proteomic, and metabolomics analyses could clearly distinguish the direction and stage of cell differentiation in A. praecox.

Proteomic Analyses of Cell Differentiation
Venn diagram shows shared and unique DAPs among five compared pairs (IEC/CA, IOC/CA, GEC/CA, REC/CA, and SEC/CA). In total, 672 proteins were significantly differentially accumulated between CA and EC of all six samples. Among these, 12 DAPs were strongly accumulated especially in IEC, 10 DAPs were accumulated specifically in IOC, and 65 were accumulated in REC ( Figure 3A).
DAPs were analyzed by hierarchical clustering using CA, IEC, IOC, and GEC as samples to identify the differentiation direction of CA. The K-means clustering approach based on the level of protein accumulation was used to sort all differential peaks resulting in 10 clusters, named C1 to C10, respectively (Supplementary Figure S1). As illustrated in Figure 3B,  DAPs (C7) were over-accumulated in IEC. As shown in Figure 3C, 49 DAPs (C2) were down-accumulated in IEC. Similarly, differentiation stage-specific patterns were present according to their relative abundance, and the number ascribed to each cluster was also recorded (Supplementary Figure S2). The results indicated that DAPs accumulated in C5 were specifically accumulated in IEC, comprising 57 proteins showing a marked increase from CA to IEC (Figure 3D).
The proteomic results via KEGG analysis revealed that different pathways were enriched concerning cell differentiation. A total of 126 pathways were enriched in comparing the IEC/ CA (Supplementary Table S1). These DAPs were involved mainly in pathways (level 2) including the metabolism of terpenoids and polyketides, xenobiotics biodegradation and metabolism, amino acid metabolism, glycan biosynthesis and metabolism, lipid metabolism, metabolism of other amino acids, endocrine system, metabolism of cofactors and vitamins, and carbohydrate metabolism. The top 20 terms are shown in Figure 3E. Similarly, 61 pathways were enriched in the comparison of IOC/CA (Supplementary Table S2). We observed that DAPs were involved mainly in pathways (level 2) including metabolism of cofactors and vitamins, energy metabolism, glycan biosynthesis and metabolism, metabolism of terpenoids and polyketides, metabolism of other amino acids, xenobiotics biodegradation and metabolism, digestive system, biosynthesis of other secondary metabolites, carbohydrate metabolism, amino acid metabolism, endocrine system, and lipid metabolism. The top 20 terms are shown in Figure 3F. A total of 42 pathways were enriched between the IOC and IEC (Supplementary Table S3). DAPs were involved mainly in the pathways (level 2) including lipid metabolism, glycan biosynthesis and metabolism, metabolism of terpenoids and polyketides, metabolism of other amino acids, amino acid metabolism, carbohydrate metabolism, xenobiotic biodegradation and metabolism, endocrine system, metabolism of cofactors and vitamins, biosynthesis of other secondary metabolites, and energy metabolism. The top 20 terms are shown in Figure 3G.
Among DAPs, top DAPs were enriched in the comparison groups of IEC/CA, IOC/CA, and IOC/IEC were listed in Supplementary Table S4. Among them, peroxidase (A0A5P1F2L4), C2H2-type domain-containing protein (A0A5P1FM85), and Histone H2B (A0A5P1FK74) were overaccumulated significantly in IOC and IEC compared to CA. Meanwhile, BTB/POZ domain-containing protein (A0A2H9ZSK5), peroxidase (A0A2I0X575), and Beta-Dxylosidase (A0A2I0V9J4) were over-accumulated considerably in IOC compared to IEC. The results above indicated that carbohydrate and energy metabolism, amino acid metabolism, secondary metabolites, chromatin accessibility, and DNA methylation may play important roles in cell differentiation.
We screened 27 pathways enriched in comparing the REC/ IEC (Supplementary Table S5). DAPs were involved in xenobiotic biodegradation and metabolism, carbohydrate metabolism, energy metabolism, glycan biosynthesis and metabolism, amino acid metabolism, lipid metabolism, energy metabolism, replication and repair, xenobiotic biodegradation and metabolism, metabolism of cofactors and vitamins, endocrine system, and aging. The top enriched pathways are shown in Figure 3H, revealing that carbohydrate and energy metabolism, aging, and genetic information processing were changed remarkably by cell differentiation stage.

Metabolomic Analyses of Cell Differentiation
Hierarchical clustering for the enriched levels of all metabolites intuitively demonstrated the stability of the relationship between QC and other samples. It showed that samples including GEC, IOC, and IEC were grouped together, REC and SEC were gathered in another cluster, and the two groups were separated from CA ( Figure 4A).
In this study, 390 DAMs were identified, which mainly included sugars, glucosides, amino acids, organic acids, and secondary metabolites. DAMs were analyzed by hierarchical clustering based on the accumulation of significantly different metabolites in each group to select marker metabolites accurately and investigate changes in the related metabolic processes (Supplementary Figure S3). A clear separation of metabolites existed between the over-accumulated and down-accumulated ones with the comparison of direction with cell differentiation.
DAMs were analyzed by hierarchical clustering using CA, IEC, IOC, and GEC as samples to identify the metabolic profile of cell differentiation direction. K-means clustering approach based on the level of metabolites accumulation was used to sort all differential peaks, resulting in eight clusters, named C1 to C8 (Supplementary Figure S4). As illustrated in Figure 4B, DAMs including sucrose, D-fructose-6-phosphate, and galactaric acid were highly accumulated in IEC. As shown in Figure 4C, DAMs including mannose 6-phosphate, glucose 6-phosphate, L-sorbose, d-mannose, galactose, and D-fructose were accumulated highly in IOC. Besides, DAMs including glucose-1-phosphate, trehalose-6-phosphate, 3-desoxy-pentitol, D-fructose-1,6-bisphosphate, and levoglucosan were highly accumulated in IEC and GEC ( Figure 4E). Differentiation stage-specific patterns were present according to their relative abundance. The DAMs were also screened by the differentiation stage. Broken line graphs were drawn to classify the accumulation patterns of DAMs, and the number ascribed to each cluster was also recorded (Supplementary Figure S5). The results indicated that DAMs accumulated in C8 were specifically accumulated in IEC samples; 29 DAMs showed a marked increase from CA to IEC. Further, these DAMs were specifically accumulated in IEC than in REC and SEC. The DAMs were enriched in the carbohydrate metabolism pathway, including D-arabinose, glucose-1-phosphate, D-fructose-1,6-bisphosphate, ribo-hexos-3-ulose, glucose, maltitol, and glucose 6-phosphate ( Figure 4D). Notably, dehydroascorbic acid was enriched in IEC, not only in the analyses of differentiation direction but also in the analyses of differentiation stage (Figures 4D,E). Interestingly, most of the CA metabolites identified during different directions and stages were enriched in the same pathways, such as sugar metabolism, arginine and proline metabolism, galactose metabolism, glyoxylate and dicarboxylate metabolism, and ascorbate and aldarate metabolism (Figures 4D,E).
All of the DAMs differing by cell differentiation were mapped to the KEGG database to analyze the relevant pathways. The results showed that 60, 60, 54, and 62 pathways were enriched in DAMs in the comparison groups of IEC/ CA, IOC/CA, IOC/IEC, and REC/IEC, respectively (Supplementary Table S6). The top 20 metabolic pathways with significant enrichment were selected for bubble mapping to understand the differential metabolic pathways of various samples by comparing DAMs in the KEGG database. The CA differentiation-related pathways were enriched as follows: ABC transporters, alanine, aspartate and glutamate metabolism, citrate cycle (TCA cycle), sulfur relay system, aminoacyl-tRNA biosynthesis, taurine and hypotaurine metabolism, arginine and proline metabolism, pentose and glucuronate interconversions, glyoxylate and dicarboxylate metabolism, purine metabolism, lysine biosynthesis, ascorbate and aldarate metabolism, carbon fixation in photosynthetic organisms, and fatty acid biosynthesis (Figure 4). The changes in these metabolites and metabolic pathways provided important information on how A. praecox responded to cell differentiation. Interestingly, the pathways including beta-alanine metabolism, starch and sucrose metabolism, cysteine and methionine metabolism, glutathione metabolism, and ascorbate and aldarate metabolism were specific in IEC/CA (Figure 4F). The pathways including cyanoamino acid metabolism, serine and threonine metabolism, pentose phosphate pathway, arginine biosynthesis, and stilbenoid, diarylheptanoid, and gingerol biosyntheses were specific in IOC/CA ( Figure 4G). Besides, DAMs related to tropane, piperidine, and pyridine alkaloid biosynthesis, butanoate metabolism, galactose metabolism, nicotinate and nicotinamide metabolism, pyruvate metabolism were differentially accumulated in IOC/IEC ( Figure 4H). The pathways including glutathione metabolism, phenylpropanoid biosynthesis, cysteine and methionine metabolism, butanoate metabolism, galactose metabolism, and valine, leucine, and isoleucine biosyntheses were significantly changed in the REC compared with the IEC (Figure 4I). Besides, plant hormones including auxin and cytokinin biosynthesis-related pathways, such as tryptophan metabolism, and zeatin biosynthesis were screened (Supplementary Table S6).
Top DAMs screened in the comparison groups of IEC/CA, IOC/CA, and IOC/IEC were listed in Supplementary Table S7. Among them, 3,4-methylenedioxyamphetamine, glucose, glycyl tyrosine sedoheptulose, lactitol, and chlorogenic acid were accumulated significantly different in the IOC compared to CA. Meanwhile, the accumulation of butyric acid, scopolamine, 3,4-methylenedioxyamphetamine, ribo-hexos-3-ulose, and glucose showed significant differences in the IEC compared to CA. Interestingly, the accumulation of glucose showed 821.098 and 772.350 times in the IOC and IEC compared to CA, respectively. The top DAMs enriched suggested carbohydrate and energy metabolism was changed remarkably in cell differentiation.

Integrated Proteomic and Metabolomic Analyses of Cell Differentiation
We performed a KEGG mapping analysis based on the dataset of DAPs and DAMs. Top 10 pathways containing DAPs and DAMs in different comparisons were listed separately (Figure 5). Further analysis of these pathways suggested that these proteins and metabolites were involved mainly in energy metabolism as well as in fatty acid degradation, pyruvate metabolism, glycolysis/gluconeogenesis, pentose and glucuronate interconversions, starch and sucrose metabolism, galactose metabolism, carbon fixation pathways in prokaryotes, carbohydrate digestion and absorption, and fructose and mannose metabolism and ROS responses and resistance, such as ascorbate and aldarate metabolism (Figures 5A,C,E,G).
We constructed an interactive diagram concerning IEC induction using an integrated analysis of DAPs and DAMs enriched in top 10 KEGG pathways. In the network, starch and sucrose metabolism pathway was enriched, and the accumulation of sugars and their intermediate products, including sucrose, glucose 6-phosphate, glucose-1-phosphate, and trehalose-6-phosphate, was enhanced significantly. Meanwhile, glucose-6-phosphate isomerase (A0A2I0WNF0) was over-accumulated and alpha-glucosidase (A0A2I0A9R5) was down-accumulated. Glucose-1-phosphate played critical roles in the connection between the pathways including starch and sucrose metabolism, glycolysis/ gluconeoproteinsis, glycerolipid metabolism, pentose and glucuronate interconversions in amino acid metabolism, and ascorbate and aldarate metabolism, accompanied by the high accumulation of glycerol 3-phosphate, glucose, D-xylulose, and xylonic acid. The metabolite glucose-1-phosphate might interact with several metabolic pathways in the integrated network ( Figure 5B).
In the IOC induction network, the enrichment of alphaketoglutarate, D-xylose, D-ribulose 5-phosphate, D-xylulose, xylonic acid, arabitol, and digalacturonic acid changed significantly in the pentose and glucuronate interconversions pathway. Meanwhile, hexitol, alpha-glucosidase (A0A2I0A9R5), and beta-glucosidase (A0A2I0VXW4) were suppressed in the galactose metabolism. Mannose 6-phosphate and D-xylose were enhanced in amino sugar and nucleotide sugar metabolism. Besides, common metabolism pathways, such as arginine and proline metabolism, were regulated in the IOC induction process ( Figure 5D).
Comparing IOC induction with IEC induction, pathways related to energy metabolism as well as fatty acid biosynthesis, fatty acid degradation, fatty acid metabolism, glycolysis gluconeogenesis, pentose and glucuronate interconversions, starch and sucrose metabolism, carbon fixation pathways in prokaryotes, and amino sugar and nucleotide sugar metabolism were connected. The enrichment pattern indicated that energy metabolism might play key roles in cell differentiation directions. The metabolites including D-xylose, pyruvic acid, alpha-ketoglutarate, malic acid, aconitic acid, alphaketoglutarate, lauric acid, myristic acid, and glutaric acid accumulated in IOC compared with IEC. However, the products, such as sucrose, glucose-1-phosphate, and arabitol decreased ( Figure 5F).
Comparing REC induction with IEC induction, pathways related to energy metabolism as well as fatty acid biosynthesis, fatty acid degradation, glycolysis/gluconeogenesis, pentose phosphate pathway, starch and sucrose metabolism, carbon fixation pathways in prokaryotes, and amino sugar and nucleotide sugar metabolism were connected. A majority of the DAPs and DAMs showed suppression in the regulated network. Metabolites including alpha-D-glucose, galactose, D-fructose, D-fructose-6-phosphate, allose, glucose, and glucose 6-phosphate decreased remarkably in the REC induction compared with IEC induction. The enrichment pattern indicated that sugar metabolism might play key roles in cell differentiation stages ( Figure 5H).
By conducting a KEGG mapping analysis based on the DAPs and DAMs, we found that cell differentiation was associated with 23 metabolic pathways (Figure 6). Integrated proteomic and metabolomic pathway analyses suggested that the regulation of sugar metabolism might affect cell differentiation direction and stage in A. praecox. The accumulation of A0A2I0WNF0 (glucose-6-phosphate isomerase), glucose-1-phosphate, glucose 6-phosphate, trehalose-6-phosphate, and sucrose, which were involved in starch and sucrose metabolism, was over-accumulated. Notably, alpha-glucosidase was down-accumulated (Supplementary Figure S6). The levels of beta-D-glucose and alpha-D-glucose 1p increased by as much as 9.59 (log 2 FC) and 1.97 (log 2 FC), respectively, in the glycolysis/gluconeogenesis pathway (Supplementary Figure S7).

Effects of Exogenous Carbon Sources on Cell Differentiation
We compared the effects of the exogenous carbon sources on cell differentiation to explore the effects of sugars metabolism in cell differentiation regulation. The transcriptomic data showed that 9,797, 5,714, and 3,921 DEGs were specifically expressed with sucrose, glucose, and maltose treatment, separately ( Figure 7A). The PCA model showed that non-EC (Suc, Glu, and Mal) and IEC samples could be separated distinctly (22.34%), and the CA cultured with sucrose had a closer relationship with the IEC (Figures 7B,C). The transcriptomic results via KEGG analysis revealed that different pathways were enriched concerning cell differentiation. Interestingly, plant hormone signal transduction, glycolysis/ gluconeogenesis, flavonoid biosynthesis, and starch and sucrose metabolism showed significantly different expression patterns when callus cultured with different carbon sources. The top terms are shown in Supplementary Figure S8. The results indicated that carbon sources might affect EC induction. Subsequently, we incubated the CA with different carbon sources ( Figure 7D). The results showed that glucose increased cell multiplication, cell mass size, and cell size, while maltose decreased growth coefficient, cell mass size, and cell size significantly. Maltose promoted organogenesis, and BP was induced evidently (Figures 7E-G). Meanwhile, EC was acquired with sucrose treatment. Further, we found that the proportion of different carbon sources (sucrose and maltose) affected the direction of cell differentiation, sucrose was beneficial to EC induction, and maltose was beneficial to organogenesis (Figures 7H-J). The results indicated that sugar metabolism affected CA differentiation direction in A. praecox, exogenous sucrose was indispensable in the process of the acquisition of embryogenic ability, and CA incubated with maltose tended to induce organogenesis.

DISCUSSION
SE is a powerful tool for plant propagation (De-la-Peña et al., 2015;Ribeiro et al., 2019), particularly cultivars in perennial ornamental species, such as A. praecox. We performed proteomic and metabolomic profiling, which could provide important insights into proteins and metabolites accumulation, and identified several SE-related pathways during EC formation in this species. Different numbers of DAPs and DAMs were specifically accumulated in IEC, IOC, and REC, suggesting that these proteins and metabolites contributed to cell differentiation in A. praecox. Prominent correlations between EC-specific pathways indicated the possible regulatory roles of carbohydrate and energy metabolism, reactive oxygen species (ROS) responses and resistance, chromatin accessibility and DNA methylation, and plant hormone signals (Aguilar-Hernández and Loyola-Vargas, 2018;Ribeiro et al., 2019;Guo et al., 2020).

Carbohydrate and Energy Metabolism
Integrated proteomic and metabolomic analyses of cell differentiation with different EC formation capabilities indicated that carbohydrate and energy metabolism might affect cell differentiation in plant regeneration (Ge et al., 2017;Aguilar-Hernández and Loyola-Vargas, 2018). Sugar and energy metabolism played key roles in embryogenic development by supporting cell division and modification (Aguilar-Hernández and Loyola-Vargas, 2018). Proteins and metabolites were accumulated differentially between IEC and IOC. Consistently, the content of glucose was higher in IEC and IOC than CA (Supplementary Table S7). The results suggested an increasing in reducing sugars, such as glucose supported the high frequencies in cell division and differentiation (Ribeiro et al., 2019). Sucrose and maltose in the culture medium significantly influenced EC induction and organogenesis induction, respectively, in A. praecox. Interestingly, the content of sucrose was higher in the IEC than IOC (Figure 4B). The results suggested that carbohydrate and energy metabolism played fundamental and significant roles in cell differentiation direction. Although carbohydrate metabolism has previously been reported to be involved in SE, the role of these proteins and metabolites is species-dependent (Aguilar-Hernández and Loyola-Vargas, 2018). Intracellular sugar metabolism was significantly affected by the carbon sources of culture medium with in vitro culture. Plant cells can be adjusted in feedback according to the demand of sugar metabolism. However, carbon sources including sucrose, glucose, fructose, and maltose in the medium had a direct influence on cell division and differentiation (Blanc et al., 2002), suggesting important roles of exogenous carbon sources in cell proliferation and differentiation (Ribeiro et al., 2019).
Sugars play roles in respiration, cellular carbon skeleton, osmotic regulation, and signal transduction in plants (Blanc et al., 2002). Our metabolite-protein correlation network incorporated proteins and metabolites that were differentially accumulated in cell differentiation direction. Primary correlations were found between sugar metabolism, energy and substraterelated proteins (alpha-mannosidase, sucrose synthase, alphamannosidase, alpha-galactosidase, NADPH dehydrogenase, beta-galactosidase, pyruvate decarboxylase, glucose-6-phosphate isomerase, and so on), and metabolites (glucose, D-xylulose, D-fructose-1,6-bisphosphate, maltotriose, sucrose, and so on). The metabolic analysis, which indicated sucrose hydrolysis activity and energy status, was higher in IEC than in CA (Aguilar-Hernández and Loyola-Vargas, 2018). This pattern confirmed a need for an increase in reducing sugars during EC induction and multiplication, to support the high frequencies of cell divisions. Notably, we also found that glucose-1-phosphate was a node connecting pathways, such as starch and sucrose metabolism, pentose and glucuronate interconversions, glycerolipid metabolism, and glycolysis/gluconeogenesis. It suggested the activation of energy metabolism in the initial stages, reflecting the need for a large amount of energy to sustain cell division (Ribeiro et al., 2019). Further, we observed that 2-oxoglutarate, which linked the TCA cycle with amino acid, glucosinolate, flavonoid, alkaloid, and gibberellin (GA) biosynthesis (Araújo et al., 2014), specifically accumulated in CA with 2.67 (log 2 FC) increase in activities compared with IEC. Differential accumulation of 2-oxoglutarate between CA and IEC indicated that cell differentiation was affected not only by the TCA cycle but also by amino acid and hormone metabolism. Altogether, sugars were critical in controlling the transition between CA, IOC, and IEC, potentially by regulating basal metabolism (Aguilar-Hernández and Loyola-Vargas, 2018). Thus, our results suggested that carbohydrate and energy metabolism cooperatively contributed to the activation of EC formation.

ROS Responses and Resistance
A complex balance exists between intracellular stress signals, hormone signals, and carbohydrate metabolism (Pasternak et al., 2007;Wójcikowska et al., 2020). ROS are readily detectable in plant responses to stresses (Fehér, 2015;Zhu, 2016). The generation and clearance of intracellular ROS are in a dynamic change manner. As signal molecules affect cell proliferation and differentiation, antioxidant enzymes and metabolic enzymes are used for intracellular ROS clearance (Ribeiro et al., 2019). Ascorbate peroxidases (APX) play an important role in ROS metabolism (Zhou et al., 2016;Huang and Li, 2020). Our results showed a correlation between ascorbate and aldarate metabolism and cell status. L-dehydroascorbate accumulation was significantly increased 1.8569 (log 2 FC) in the IEC than in the CA, reflecting the important role of ROS scavenging in EC competence acquisition (Fehér, 2015). The levels of antioxidative proteins, such as APX and glutathione-S-transferase, are frequently higher in the IEC than in the CA; this has been regarded as a determinant of cell embryogenic capacity (Zhou et al., 2016;Huang and Li, 2020). However, glutathione-S-transferase also participates in cell signaling, catalase in sugar or amino acid metabolism, and ascorbate peroxidase in the glutathione-ascorbate cycle; all were activated in embryogenic tissues (Żur et al., 2019). Thus, these enzymes may contribute to embryogenic capacity through various developmental and cell cycling processes rather than merely through the deactivation of ROS (Zhou et al., 2016). Moderate-intensity ROS, such as H 2 O 2 , usually promotes the in vitro culture and redifferentiation of plant cells (Zhou et al., 2016), while excess ROS cause membrane lipid peroxidation and lead to cell electrolyte disorder (Zeng et al., 2017). On the contrary, stress upregulated the expression of modifying enzymes, such as expansins and xyloglucan, that remodeled the cell wall (Zhu, 2016), which ultimately affected cell enlargement and division. These stress defense reactions have been interpreted as responses of the tissues to in vitro culture conditions (Zhao et al., 2015). They are probably not directly related to a specific morphogenic pathway, such as SE, but may be prerequisites for embryogenesis. Indeed, the proliferation and differentiation of cell clusters provided the basis of regeneration, and strong resistance for ROS provided the possibility of regeneration.
Our result confirmed that defense responses against abiotic stress modulated embryogenic capacity (Fehér, 2015 Figure S9). The embryogenic nature of somatic cells is a prerequisite for auxin-induced chromatin accessibility and transcriptome alteration; auxin promotes the acquisition of plant cell totipotency in SE development pathway (Fraga et al., 2016;Wang et al., 2020;Wójcikowska et al., 2020). Plant growth regulators (PGRs) modify the levels of DNA methylation in the SE process, such as EC induction (De-la-Peña et al., 2015;Fraga et al., 2016). Auxin rapidly rewires the cell totipotency network by altering chromatin accessibility (Wang et al., 2020;Wójcikowska et al., 2020). Auxin-related PGRs have pleiotropic effects in cell proliferation and differentiation (Osorio-Montalvo et al., 2018;Ikeuchi et al., 2019;. Endogenous active IAA plays an important role in the association between the expression of key factors inducing cell differentiation and the epigenetic regulatory network (Tang et al., 2020). PIC and 2,4-dichlorophenoxyacetic acid (2,4-D) are used for endogenous IAA biosynthesis and auxin signal regulation in plants, such as A. praecox and A. thaliana (Horstman et al., 2017). The inductive stimuli evoke endogenous auxin biosynthesis, leading to increased auxin levels and inducing totipotency in somatic cells (Su et al., 2021). Local auxin biosynthesis and polar auxin transport are essential for establishing auxin gradients during SE formation (Su et al., 2009(Su et al., , 2021Bayer et al., 2017;. It is widely accepted that the balance of auxin and cytokinin contents is critical for cell differentiation in different plants by regulating plant hormone signaling-related genes and methylation levels (Li et al., 2019;Zhao et al., 2021). However, few hormone-related DAMs were enriched in this study due to the extremely low hormone content in plant cells. Interestingly, the combination of PIC and 6-benzylaminopurine (6-BA) promoted organogenesis significantly (Supplementary Figure S10). The auxin gradients appear to activate PIN1 polar localization within the embryonic callus in A. thaliana, and polarized PIN1 is probably responsible for the polar auxin transport and accumulation in somatic embryos.
The establishment of auxin gradients and PIN1-mediated polar auxin transport are essential for SE in A. thaliana (Su et al., 2009). The auxin-and CTK-related DEGs regulated SE capacity in plants, such as G. hirsutum (Xu et al., 2013) and Passiflora edulis (Rocha et al., 2015). In this study, auxin signal transductionrelated DEGs including auxin response factor (ARF), auxin/ indoleacetic acids (AUX/IAAs), GH3, and transport inhibitor response1 (TIR1), CTK signal transduction-related genes (e.g., ARR-A, ARR-B, and CRE1) transcripts were differentially expressed with sucrose and maltose treatment (Supplementary Figure S11). Taken together, chromatin accessibility and DNA methylation, and plant hormone signal were critical for EC induction in A. praecox. Based on the aforementioned discussion, we hypothesized an EC induction model in A. praecox (Figure 8). In summary, CA undergone differentiation direction and stage showed different energy supply, stress response, hormone signal transduction, and chromatin accessibility level. All these differences might be responsible for the acquisition of embryogenic ability in A. praecox.

CONCLUSION
Altogether, this study revealed valuable information regarding the molecular and biochemical changes that occurred during EC induction, and provided valuable foundation for comprehensive understanding of the mechanisms associated with SE and organogenesis in A. praecox. Using network analysis, we identified the potential key DAPs and DAMs with cell differentiation. Carbohydrate and energy metabolism was critical in controlling the transition between CA and EC, potentially by regulating basal metabolism, removal of ROS, chromatin accessibility, and plant hormonal signaling. We also assessed the roles of carbohydrate metabolism and plant hormone in cell differentiation direction. Thus, our results suggested that carbohydrate and energy metabolism cooperatively contributed to the activation cell totipotency and cell pluripotency, thereby inducing the EC and organs. Future research should focus on the possible interactions of carbohydrate metabolism, ROS responses and resistance, chromatin accessibility, and plant hormone signals, which might have a major impact on the acquisition of embryogenic ability in A. praecox.

DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: National Center for Biotechnology Information (NCBI) BioProject database under accession number PRJNA800607. The proteomic data was deposited in ProteomeXchange (Dataset PXD031207).


s2fieldsofstudy: Biology Medicine Chemistry

text:
An update on antibody–drug conjugates in urothelial carcinoma: state of the art strategies and what comes next

In recent years, considerable progress has been made in increasing the knowledge of tumour biology and drug resistance mechanisms in urothelial cancer. Therapeutic strategies have significantly advanced with the introduction of novel approaches such as immune checkpoint inhibitors and Fibroblast Growth Factor Receptor inhibitors. However, despite these novel agents, advanced urothelial cancer is often still progressive in spite of treatment and correlates with a poor prognosis. The introduction of antibody–drug conjugates consisting of a target-specific monoclonal antibody covalently linked to a payload (cytotoxic agent) is a novel and promising therapeutic strategy. In December 2019, the US Food and Drug Administration (FDA) granted accelerated approval to the nectin-4-targeting antibody–drug conjugate, enfortumab vedotin, for the treatment of advanced or metastatic urothelial carcinomas that are refractory to both immune checkpoint inhibitors and platinum-based treatment. Heavily pre-treated urothelial cancer patients reported a significant, 40% response to enfortumab vedotin while other antibody–drug conjugates are currently still under investigation in several clinical trials. We have comprehensively reviewed the available treatment strategies for advanced urothelial carcinoma and outlined the mechanism of action of antibody–drug conjugate agents, their clinical applications, resistance mechanisms and future strategies for urothelial cancer.

Introduction
The treatment options for advanced urothelial cancer (UC) have been rapidly developing over the last few years. This development began with the approval of anti Fibroblast Growth Factor Receptor (FGFR) and various immune checkpoint inhibitors (ICIs), followed by the Food and Drug Administration (FDA) approving enfortumab vedotin (EV), an antibody-drug conjugate (ADC) for the treatment of advanced urothelial carcinoma in 2019 [1]. Whilst EV is the first ADC to gain FDA approval for the treatment of UC, their use is not novel and are commonly used for the treatment of breast cancer [2,3] and hematologic malignancies [4,5]. Given the positive results observed with EV in patients with advanced UC, several clinical trials are underway with the aim of demonstrating the improved efficacy of ADCs and with the ultimate goal of approving the ADC use in earlier lines of therapy. This review will provide an overview of current treatment options for advanced urothelial carcinoma and details of the clinical development of various ADCs being studied for use in this cancer type.

Urothelial carcinoma
Urothelial cancer is the fourth most common cancer among American males with over 80,000 cases diagnosed in 2020 [6]. UC normally occurs in older patients (7th decade and older). Risk factors for development include a genetic predisposition (such as Lynch syndrome), chemical and environmental exposure (cyclophosphamide, aromatic amines), cigarette smoking and male sex [7]. The vast majority of urothelial cancers arise within the bladder and are found to have not yet invaded the muscle at diagnosis [8]. First-line treatment for non-muscle invasive bladder cancer (NMIBC) (Ta/Tis/T1) is generally surgical via transurethral resection of bladder tumour (TURBT) with additional intravesical therapy using bacillus Calmette-Guerin (BCG) or mitomycin, in order to prevent either disease relapse or regression [9,10].
Even though a large number of patients benefit from the aforementioned strategy, a fraction of these patients will progress to muscle-invasive bladder carcinoma (MIBC), a locally advanced disease stage that is associated with a high rate of lymph node spread and distant metastasis [11]. In these cases, the gold standard treatment involves neoadjuvant cisplatin-based chemotherapy (although still poorly adopted compared to adjuvant chemotherapy) followed by radical cystectomy, a surgical procedure associated with non-trivial mortality and a significant effect on quality of life [12]. As a result, it is clear that in certain cases there is a significant unmet clinical need and thus an opportunity for future drug development.

Treatment of advanced muscle-invasive urothelial carcinoma
Systemic treatment is required for patients with advanced or metastatic urothelial cancer (mUC). Until recently, cisplatinbased combination therapies were the only option available. Examples included: methotrexate, vinblastine, adriamycin and cisplatin (MVAC), or gemcitabine and cisplatin (GC). In a phase III trial, both regimens demonstrated a similar response rate of approximately 40-50% and a 5-year survival rate of 10-15%. However, GC regimens reported lower toxicity and are therefore currently the treatment of choice in ongoing trials [13]. Conversely, regimens containing carboplatin (usually gemcitabine plus carboplatin), are the preferred treatment for those deemed cisplatin-ineligible, with similar response rates to GC regimens, but with poorer survival outcomes [14,15].
Further research in this setting has led to the development of immune checkpoint inhibitors, such as atezolizumab, avelumab, durvalumab, nivolumab and pembrolizumab; all of which are now can be used in patients with UC [16]. Phase III trials recently demonstrated that pembrolizumab treatment resulted in a 3 months benefit in terms of survival when compared to standard of care taxane or vinflunine in platinum-ineligible patients [17,18]. Notably, in another phase III trial, atezolizumab showed no benefit on overall survival. Nonetheless, both atezolizumab and pembrolizumab were granted FDA approval for their use as first-line agents for platinum-ineligible patients with UC [19].
In addition to immunotherapy options, second-line regimens including antifolates and taxanes represent a valid option for patients who have had disease progression on platinum-based regimens, although these regimens do report poor response rates of approximately 15% [17]. Additionally, a vinflunine agent reported a debatable improvement in survival when compared to the standard of care in a phase II trial, achieving European Medicines Agency but not FDA approval [20].
Moreover, significant treatment advancements have been made for those metastatic UC patients (20%) harbouring mutations in the FGFR pathway. Erdafinitib, an oral FGFRinhibitor, reported a response rate of 40% in patients with FGFR alterations in phase II single study, leading to accelerated FDA approval for use in patients who have progressed on platinum-based therapy [21,22]. Despite these developments, a large number of patients still relapse following platinum-based regimens as well as immunotherapy. Consequently, this unmet clinical need led to the generation of antibody-drug conjugates, which have generated a robust interest within the scientific community.

Antibody-drug conjugates (ADCs)
ADCs are small molecule anticancer agents covalently linked to a monoclonal antibody (mAb). Specific antigens expressed on tumour surfaces are targeted by the mAb, resulting in selective delivery of the anticancer agent to tumour cells [23] (Table 1). Excessive toxicity or poor handling limits some chemotherapeutic drugs from being used as classical chemotherapeutic agents [24]. ADCs can overcome this limitation by selectively delivering cytotoxic agents to tumour targets, reducing toxicity, and increasing efficacy. The early development of ADCs involved the use of murine antibodies conjugated with chemotherapeutic agents such as methotrexate, doxorubicin, and vinblastine, even though these agents have limited selectivity and strong immunogenicity [25]. As the technology has evolved and humanised antibodies developed, these agents have become more effective and specific, leading to improved potency and reduced immunogenicity [26]. Three components make up an ADC; an antibody specific for the target antigen, a linker domain and then the cytotoxic agent.

Antibody identification
ADCs are not required to elicit an immune response after linking with the cytotoxic payload [27]. Presently, immunoglobulin G forms the integral structure and is composed of four subclasses (IgG1, IgG2, IgG3, and IgG4), each of which differ from each other in the structure of the constant domain and hinge regions [28,29]. Most immunotherapies, including ADCs, utilise IgG1 as it can stimulate immune effector functions (receptor binding, endocytosis and downstream activation of immune pathways). IgG1 also has the advantage of high stability in serum, and low molecular weight and is well distributed in the intra-and extravascular space [26] (Fig. 1). The mAb should be targeted against an antigen strongly expressed on malignant cells, and absent on non-malignant cells, this is crucial in reducing systemic toxicity and widening the therapeutic window. Examples of such antigens within UC cells include HER2, Nectin-4 and Trop-2. Furthermore, there should be limited antigen cross-reactivity, with strong binding affinity to the target to ensure effective internalisation and stability [30,31].

Drug-mAb linker
The linker acts to join the cytotoxic agent to the antibody via the conjugation sites in the antibody heavy chains. Two crucial characteristics must be present for the Linker to be functional. Firstly, the linker must ensure the antibody and cytotoxic agent remain firmly bound, particularly in the plasma during circulation. An unstable linker may lead to premature delivery of the cytotoxic agent into the systemic circulation leading to unwanted toxicity and reduced therapeutic efficacy [32]. Secondly, the linker must be able to deliver the drug once at the tumour site [33].
There are two main sub-classes of linkers; cleavable and non-cleavable. Cleavable linkers rely on factors in the tumour microenvironment to stimulate the breakdown and release of the ADC cytotoxic agent [34]. Mechanisms of linker cleavage are diverse; one such mechanism is driven by glutathione (highly represented in the cytoplasm compared to the extracellular space), which leads to the release of the cytotoxic agent via breakage of disulphide bonds [35,36]. A second type of mechanism involves linkers that cleave in environments with an acidic pH, such as hydrazone. This type of linker exploits the acidic pH found in endosomes and lysosomes. However, premature cleavage of these linkers into the circulation may lead to hepatotoxicity such as that described in gemtuzumab ozogamicin [32,37]. A third type of linker is those that are protease-dependent and are degraded by lysosomal proteases after recognition of a specific peptide sequence. These linkers allow the ADC to be remarkably stable within the plasma and thus avoid a premature release of the cytotoxic agent. Examples of this type of linker include enfortumab vedotin, ADC directed against nectin-4, and sacituzumab govitecan (SG) directed against the human trophoblast cell surface antigen 2 (Trop-2) [38].
Cleavable linkers are less stable than non-cleavable linkers. Non-cleavable linkers rely on the degradation of the complete antibody-linker complex to release the cytotoxic agent. Examples of ADCs with non-cleavable linkers are belantamab mafodotin and trastuzumab emtansine (T-DM1) [39].

Payloads
The cytotoxic agents in ADCs are often referred to as the payload, and these drugs are usually heavily toxic molecules [40]. The antibody acts as the delivery mechanism of this payload to the tumour target. The early ADCs could deliver classical chemotherapeutic agents, such as methotrexate, vinca alkaloids and doxorubicin [41,42]. However, ADCs delivering these agents did not demonstrate higher efficacy than when these agents were delivered as standard chemotherapeutic agents [43].
ADC payloads can be split into different macro-categories. The most important of which are the agents that destabilise microtubules, such as auristatins and maytansins, which are derived from natural bacteria. Monomethyl auristatin E and F (MMAE, MMAF) are examples of auristatins and are synthetic derivatives of the dolastatin 10 peptide which is isolated from Dolabella auricularia [44]. These drugs act by inhibiting the polymerisation of tubulin, resulting in cell cycle arrest and then apoptosis. Maytansins such as DM1 act in a similar fashion and target tubulin via the vinca alkaloid binding site, subsequently leading to a blockade of mitotic replication and then cell cycle arrest and apoptosis [45,46].
Other types of payload include those that act directly on DNA damage, examples include cuocarmicins and pyrrolobenzodiazepines. These agents can generate DNA double helix damage and act as alkylating agents leading to disruption of transcription, causing DNA double helix breakage and apoptosis [47][48][49]. Further examples of this type of payload include the camptothecin analogues, such as SN-38, which can inhibit topoisomerase I resulting in DNA damage and breakage.
Additionally, ADC activity relies on a well-balanced drug-to-antibody ratio (DAR). A high DAR can negatively impact pharmacokinetics [50], whereas a low DAR may reduce ADC potency. ADCs with high DARs may show greater efficacy and internalisation, but this may also lead to increased clearance [51][52][53]. Importantly, ADCs with lower immunogenicity have a key advantage in that these are less likely to lead to the development of anti-drug antibodies, the presence of which can suppress drug efficacy [54].

Enfortumab vedotin
Enfortumab vedotin is a novel ADC composed of a fully human antibody, targeting Nectin-4 and the potent microtubule-disrupting agent monomethyl auristatin E [55]. Nectin-4 is a junction protein implicated in cell-cell adhesion [56]; it is involved in a variety of biological processes such as tumour-cell growth, proliferation, immune modulation and viral entry [57]. In a previous study, it has been found that Nectin-4 mRNA, a poliovirus receptor-related protein-4 (PVRL4), is highly expressed in cancer cells, especially in bladder cancer (BC) [58] and that such aberrant expression is associated with cancer progression and poor prognosis. Due to its central role in tumorigenesis and lymphangiogenesis, it has emerged as a potential biomarker and promising targeted therapy. In 2020, a phase III trial has investigated the efficacy of EV versus investigator-choice chemotherapy (docetaxel, paclitaxel, and vinflunine) in 608 patients progressing after platinum-containing chemotherapy and ICI [55]. At the prespecified interim analysis, the primary endpoint was met with longer overall survival (OS) in the enfortumab vedotin group than in the chemotherapy group [median OS, 12.8 vs. 8.9 months; hazard ratio (HR) for death, 0.70; 95% confidence interval (CI), 0.56-0.89; p = 0.001). After these results, enfortumab vedotin was granted EMA and FDA breakthrough therapy approval for the treatment of patients previously treated with platinum-containing chemotherapy and ICI [59].
Moreover, in the first-line setting, EV had a synergistic effect when combined with ICI, based on the results of the ongoing phase Ib/2 EV-103 trial, demonstrating an objective response rate (ORR) of 73%, with 15.6% of complete responses (CR) and median progression-free survival (PFS) of 12.3 months in cisplatin-unfit patients [60]. It is noteworthy to mention that EV is correlated with severe cutaneous adverse reactions, including fatal cases of Steven Johnson Syndrome or Toxic Epidermal Necrolysis, especially during the first cycle of treatment but may occur later, as well as hyperglycemia, pneumonitis, peripheral neuropathy, ocular disorders, infusion-site extravasation, and embryofetal toxicity [55,61].

Sacituzumab govitecan
Sacituzumab govitecan is a humanized anti-Trop2 monoclonal IgG1k coupled to the cytotoxic payload, SN-38, the active metabolite of irinotecan and a topoisomerase I inhibitor [62] via a cleavable linker [63,64]. Trop-2 is a 40-kDa transmembrane glycoprotein that was first discovered in human trophoblast and choriocarcinoma cell line [65]. Due to its short intracytoplasmic tail, it is correlated with several pathways regulating cellular functions such as cell-cell adhesion, cell proliferation and mobility [65,66]. Moreover, a high Trop-2 expression has been found in different cancers including urothelial cancer where it is associated with aggressive progression and poor survival outcome [67]. The TRO-PHY-U-01 study is an open-label, single-arm phase II study designed to confirm the SG antitumor activity in patients with metastatic UC who progressed after prior platinumbased and checkpoint inhibitor-based therapies [68].
Among the 113 patients who received SG, central review evidenced an ORR of 27% with an mPFS and mOS of 5.4 months and 10.9 months, respectively; thus confirming the results from the prior phase I/II study showing that SG has significant anticancer activity in heavily pretreated patients [69]. Regarding the adverse events (AEs), it is worth mentioning that SG is generally well tolerated and the observed grade 3 or greater AEs were neutropenia (35%) followed by leukopenia (18%), anaemia (14%), diarrhoea (10%), and febrile neutropenia (10%). Based on this preliminary data, SG received accelerated approval in heavily pretreated patients with mUC who had progressed on platinum and ICIs.

Sirtratumab vedotin (ASG15-ME)
Sirtratumab vedotin is an ADC composed of a SLITRK6specific human gamma 2 antibody (Igγ2) conjugated to a small molecule microtubule disrupting agent, monomethyl auristatin E (MMAE) via a protease-cleavable linker [70]. It enables the release of this MMAE to tumours expressing SLITRK6 [71]. This protein belongs to a neuronal transmembrane protein family regulating the growth and survival of neuronal cells in the inner ear that transmit auditory signals. Therefore, mutations in this gene lead to myopia and progressive auditory neuropathy in humans and mice [72,73]. Several immunohistochemical studies have demonstrated that SLITRK6 is expressed in a variety of epithelial tumours, including lung cancer, glioblastoma and breast cancer, and that it is moderately negatively correlated with tumour malignancy [74].
The first study that reported data on SV anti-tumour activity was a phase I study that included 51 metastatic urothelial cancer patients. SLITRK6 expression was evaluated by immunohistochemistry and results demonstrated it to be positive in 93% of patients. Among the 42 patients treated with a therapeutic dose (> 0.5 mg per kg), 1 showed CR at 39 weeks and 13 had a partial response (PR), resulting in an ORR of 33%. The median duration of response (DOR) and mPFS were 15 and 16 weeks, respectively. SV was generally well tolerated; fatigue was the most common grade 3 or higher AEs, evaluated in 44% of patients [71]. Ten patients experienced reversible ocular toxicities with one grade 3 toxicity. Despite these results, no current ongoing trials are evaluating the SV efficacy in UC metastatic setting.

Human epidermal growth factor receptor 2 (HER2)-ADCs in bladder cancer
HER2 has a firmly established oncogenic potential in both preclinical and clinical settings, especially in breast cancer [75]. When overexpressed, it leads to the autophosphorylation of tyrosine residues within the cytoplasmic domain of the heterodimer and triggers a complex pathway, resulting in a strong pro-tumorigenic signalling cascade [76]. Recently, various ADCs targeting HER2-positive BC have been investigated, leading to a significant improvement in survival outcomes [77]. Beyond breast and gastric cancer, urothelial carcinoma is the third most prevalent cancer with HER2 overexpression, showing potential utility for HER2-targeting therapy in mUC. Notably, it has been shown that HER2 overexpression was observed in 9.2-12.4% of invasive bladder carcinoma, with 5.1% of those demonstrating a HER2 gene amplification [78]. In addition, Fleischmann et al. demonstrated that HER2 amplification was significantly more frequent in lymph node metastases (15.3%) than in matched primary bladder cancers as well as being more apparent in the luminal than in the basal subtypes [79]. Moreover, previous studies demonstrated that in bladder cancer, HER2 overexpression strongly correlated with tumour progression and poor prognosis and, unlike BC, HER2 genomic amplification is not a common mechanism [80,81]. While in BC the role of HER2-targeting agents has been well defined in both metastatic and adjuvant settings, the efficacy of HER2-targeting agents in bladder carcinomas still remains a challenge.
Although T-DM1 showed promising antitumor effects in preclinical models of HER2 overexpressing bladder cancers [83], the multi-histology phase II, basket trial of TDM-1 in patients with HER2 amplified cancers failed to demonstrate a significant activity of this drug in patients with mUC..

Trastuzumab deruxtecan (T-DXd). T-DXd (DS-8201)
is an antibody-drug conjugate that is composed of a humanized monoclonal antibody specifically targeting HER2 linked to potent topoisomerase I inhibitor as the cytotoxic drug (payload) [3]. In the DESTINY-Breast01 trial, DS-8201 showed durable antitumor activity in a pretreated patient population with HER2-positive metastatic breast cancer [3]. Moreover, DS8201 have demonstrated a satisfactory efficacy in patients with metastatic BC HER2 low-expressing [3,84].
Disitamab vedotin.Disitamab vedotin, previously known as RC-48, is a novel ADC consisting of a humanized monoclonal antibody directed against HER-2 conjugated to MMAE via a cleavable linker [85]. In 2021 Shent et al., in a phase II study, evaluated the efficacy and safety of RC-48 in 43 patients with HER2 + (IHC 3 + and 2 +) locally advanced or metastatic UC refractory to standard therapies. They demonstrated a promising efficacy of RC-48 observing an ORR of 51%, an mPFS and mOS of 6.9 and 13.9 months, respectively, with a manageable safety profile [80]. This trial observed a higher ORR compared to historic response rates of currently available ICIs in the second-line setting. Indeed, another phase II trial, enrolling 100 patients, is underway to evaluate whether RC-48 works to treat HER2 expressing urothelial cancer (NCT04879329).

Mechanism of resistance
Little is currently known about potential resistance mechanisms against ADC treatment in UC. Further investigation is needed to shed light on drug-intrinsic mechanisms and streamline the identification of predictive biomarkers of drug efficacy. Preliminary results link ADC resistance to various biochemical mechanisms including alteration of the cell cycle, loss of payload efficacy, alteration of vesicle pathways and prevention of antibody attachment and loss of target antigen [16,33].

Impairment of cell-cycle
It is well established that the cell cycle plays a pivotal role in generating novel resistance mechanisms and a recent study showed that the expression of cyclin B is significantly increased in TDM-1-resistant cells [86]. Furthermore, modifications of the apoptosis pathway might interfere with the efficacy of ADCs. There is evidence of overexpression and mutation of BCL-X and BCL-2, plus impairment of protein regulation of BAX and BAK pathways in patients treated with Gemtuzumab ozogamicin [87].

Inhibition of payload efficacy
A common mechanism of resistance has been shown to arise following mutations occurring in the molecular target of the payload. For example, the decreased success of SG treatment might be due to resistance mutations in topoisomerase-1 [88]. In addition, ATP-binding cassette (ABC) transporters are deemed to be a frequent mechanism of chemotherapy resistance, acting to increase drug discharge from the cell microenvironment [89]. Several ADC payloads are targeted against ABC efflux transporters, thus conferring resistance to ADC treatment [90,91]. Myatansinoids and auristatin analogues have been previously reported to be substrates for ABC transporters including multidrug resistance-1 (MDR-1) in preclinical data. Exposing the cell to these agents can result in the overexpression of MDR-1 efflux transporters [92].

Impairment of vesicle pathways
There is preclinical evidence of decreased treatment sensitivity resulting from the internalization of TDM-1 into caveolin-1-coated vesicles [93]. Although the antibody internalisation into the cell (by endocytosis) is required to promote ADC efficacy, this process might curb payload efficacy. It has been shown that the internalization process might take place by means of clathrin-caveolin-independent, clathrinmediated, and caveolin-mediated endocytosis mechanisms [94].

Loss of target antigen
Loss or reduction of target tumour antigen can occur due to a multitude of reasons. Examples include; gene mutation resulting in antigen concealment to the immune system or downregulation of target gene expression or clone selection of those tumour cells with lower target antigen expression. These mechanisms are a common hurdle to maximising ADC treatment efficacy [95] as the loss or reduction of cancer target antigen might result in the release of payload or loss of antibody binding. A study carried out on patients with metastatic triple-negative breast cancer (TNBC) showed that the loss of Trop-2 expression was associated with decreased response to SG treatment [88]. The ASCENT trial compounded that finding by demonstrating that metastatic TNBC patients with high Trop-2 expression, treated with SG, reported better outcomes when compared to those with low or absent Trop-2 expression [96]. Comparably, the EMILIA trial (results of which led to TDM-1 approval for HER-2-positive metastatic cancer patients) observed that patients with higher expression of HER-2 mRNA reported better outcomes when compared to those with lower HER-2 mRNA levels [97]. Nonetheless, preliminary research studies are underway to verify the efficacy of bispecific antibodies (those able to target multiple antigens) with the ultimate goal of overcoming this particular resistance mechanism.

The role of the tumour microenvironment (TME)
Recent evidence showed that the TME plays a pivotal role in regulating tumour progression, metastasis, immune escape, and it is involved in acquired resistance of tumours to various therapies, resulting in reduced treatment efficacy [98,99]. Several mechanisms within the TME are deemed to lead to drug resistance. For instance, hypoxia and impaired blood supply, which results from the uncontrolled proliferation of tumour, are a cornerstone of TME in all solid tumours [100]. Hypoxia and impaired blood supply result in abnormal angiogenesis, inflammation and desmoplasia, all of which contribute to tumour progression and therapeutic resistance [101]. Additionally, hypoxia promotes decreased pH in the TME which supports multi-drug-resistances strategies including reduced apoptotic rate, increased activity of multidrug transported p-glycoprotein (P-gp/MDR1)("drug efflux pump"), genetic alterations such as p53 mutations, and decreased concentration of the drug due to "ion trapping"-namely the inability for charged drugs to diffuse through cells [102].

Future options to overcome resistance and optimise ADC-based therapy
Although three new ADCs have been recently granted approval for the treatment of solid cancers, a major limit to ADC clinical success is resistance to these drugs. Nonetheless, ADC modular structure and the biochemical improvements will allow soon the development of new agents capable of overcoming resistance. Indeed, ADCs engineering has recently introduced new payloads, linkers and the development of a novel generation of ADCs with an increased drugto-antibody ratio (DAR) and solid bystander effects [103]. The use of novel cleavable linkers in association with membrane-permeable payload can improve the efficacy of the bystander effect, enabling ADCs to be active against targetnegative cells, namely expanding ADC efficacy to cancer with low target expression or on heterogeneous tumours [36,104]. Another strategy that can be used is the increase of linker hydrophilicity, which can impair drug resistance as P-gp/MDR1 binds to hydrophobic compounds more efficiently than hydrophilic compounds [105]. Other studies have attempted to improve the stability of ADCs in plasma by altering the composition of linkers, focusing on replacing the most susceptible to degradation linker components with more stable substitutes [106].
Additional strategies have recently been investigated to expand the group of patients who might benefit from the newer generation of ADCs. Noticeably, new potential targets including proteins expressed by cancer stem cells (CSC), such as PTK7, ephrin-A4, 5T4 and in the TME, such as CD205, CD25, B7-H3, are under investigation with some of these that already reached clinical phases of drug development [107,108]. Bispecific and biparatopic antibodies are also under investigation in preclinical studies. While bispecific antibodies can recognize two different antigens on the same antigen, biparatopic antibodies bind two nonoverlapping epitopes of the same antigen. Additionally, a newer thread of research is focusing on smarter vehicles for payloads [109]. Probody drug conjugates are a novel group of ADC prodrugs that can be activated following proteolytic cleavage by TME proteases to minimise on-target/offtumour toxicity [110].
More importantly, the most promising results are coming from several ongoing trials investigating the efficacy of novel ADCs in combination with several targeted agents such as immune checkpoint inhibitors (ICIs).The advent of ICIs and the more recent introduction of FGFR-targeted therapy have significantly altered the treatment paradigm of advanced urothelial cancer. Additionally, the even more recent introduction of ADCs (and the FDA approval of EV) are at the forefront of urothelial cancer treatment with encouraging preliminary and clinical data. ADCs have a multitude of benefits as treatment options. Firstly, response rates are promising (so far demonstrated to be between 30 and 60%) and comparable to current cisplatin-based firstline regimens. Secondly, response to ADCs has been shown to be durable with manageable toxicity. Finally given the universal expression of urothelial cancer targets, a large fraction of patients would most likely be considered eligible or appropriate for ADC treatment. With that in mind, what does the future hold for ADC treatment in urothelial cancer? It is highly likely that additional ADC agents will be granted approval for advanced urothelial cancer treatment, particularly as interest moves toward studying the outcome of ADC therapy in treatment-naïve patients.
Furthermore, the combination of ICIs and ADCs is a strategy that needs to be considered, as the biochemical effect is potentially synergistic with no overlapping toxicity. For example, ADCs can promote cell death, resulting in cancer antigen release, leading to immune system activation and an increase of antigen-presenting cells [111]. At the same time, ICIs can regulate immunosuppression within the tumour microenvironment by modulating cytokines, enzymes and T immune cells with immunomodulatory functions [112]. Therefore, ADC agents have potential synergistic activity in combination with ICIs. The ability of ADCs to modulate the immune system is under investigation in preclinical studies. Gardai et al. reported that ADCs with an MMAE payload can trigger anti-tumour immunity and stimulate immune cell death by induction of damage-associated molecular patterns (DAMPs) on the cell surface which, in turn, can trigger the immune system [113]. Further research in murine models has shown that tumour shrinkage was increased with a PD1 inhibitor and brentuximab combination treatment, corroborating the potential synergism of the two drug classes [114]. According to this preclinical data, it can be fairly confidently assumed that a combination of ADC and ICI might lead to a stronger antitumour response in vivo. Notably, combination treatment with ICIs and ADCs has already been granted regulatory approval for several cancers, and different combinations of these two agents are currently under clinical trial investigation. The EV-103 trial, a multi-arm phase Ib/II study investigating the efficacy of EV alone or in association with chemotherapy and/or pembrolizumab in locally advanced urothelial cancer, has reported a greater benefit in terms of ORR (73%) and mPFS (12.3 months) in the first-line setting when combinations are used [115].
In addition, the EV-304 trial, a randomized phase III open-label study in cisplatin-eligible patients is underway for the investigation of early-setting efficacy in patients treated with either EV plus pembrolizumab or neoadjuvant cisplatin in combination with gemcitabine [116]. Another phase III global study, VOLGA, is testing the efficacy and safety of neoadjuvant treatment with EV plus durvalumab and tremelimumab or EV plus durvalumab in cisplatin-ineligible MIBC [117]. As far as the development of novel ADCs is concerned, different agents including integrin β6, EGFR, B7-H1 and CD25 are being evaluated in early phase basket trials with the ultimate goal of better understanding and overcoming primary and acquired resistance mechanisms as well as limiting toxicity (Table 2).

Conclusion
Until recently, chemotherapy was the only treatment available for advanced or mUC. However, over recent years, UC treatment has benefited from multiple advances, and now more targeted therapy exists, in the form of immunotherapy. However, the outcome for these patients remains poor in the long term. ADCs are innovative drug agents, which allow conventional cytotoxic therapies to be transformed into highly targeted chemotherapeutics, potentially enabling better outcomes and reduced toxicity. ADCs offer particular promise in UC as we know that multiple tumour-specific antigens are highly expressed. As a result of this specificity and potential efficacy, ADCs offer a renewed hope for those malignancies with limited therapeutic strategies such as locally advanced or metastatic UC. The ongoing drug development within approved clinical trials will elucidate the optimal sequencing or combination of these drugs. The end goal is a personalized approach to the treatment of urothelial cancer, resulting in improved outcomes for patients.
Funding This paper is not funded.

Declarations
Conflict of interest We know of no conflicts of interest associated with this publication.
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